NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction.

Area of Science:

• Neuroimaging
• Machine Learning
• Cloud Computing

Background:

• Software as a Service (SaaS) is increasingly adopted for software distribution.
• Neuroimaging-based age prediction holds potential for clinical applications.
• Accessible tools are needed for developing and evaluating predictive models.

Purpose of the Study:

• To apply the SaaS approach to neuroimaging-based age prediction.
• To introduce the Neuroanatomical Age Prediction using R (NAPR) system.
• To facilitate transparent evaluation of age prediction models.

Main Methods:

• Developed NAPR, a cloud-based system using Amazon Web Services (AWS).
• Enabled external users to estimate age from T1-weighted MRI-derived cortical thickness maps.
• Trained two age prediction models using relevance vector machines and Gaussian processes on datasets totaling 2367 subjects (ages 6-89).

Main Results:

• Successfully implemented a SaaS framework for neuroimaging age prediction.
• Trained models utilizing cortical thickness data processed with Freesurfer v5.3.
• Demonstrated the feasibility of cloud-based neuroimaging analysis for age estimation.

Conclusions:

• The NAPR system provides a transparent platform for age prediction model development and evaluation.
• This approach can accelerate the creation of more accurate neuroimaging-based age prediction tools.
• Facilitates robust assessment of the clinical utility of neuroimaging age prediction methods.